MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models. |
| Approach: | They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
| Outcome: | The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
Similar Papers
Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
Copied to clipboard
| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
| Outcome: | The proposed framework is able to successfully transfer language knowledge even when the target language corpus is limited. |
Improving Zero-Shot Multilingual Text Generation via Iterative Distillation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches to generalize multilingual dialogue systems to multilingual settings often make assumptions about data availability. |
| Approach: | They propose to transfer inductive biases for target languages learned by pretrained teacher models to student models via sequence-level knowledge distillation. |
| Outcome: | The proposed method performs well on the multiATIS++ benchmark, and is comparable to human annotations in both slot F1 and intent accuracy. |
Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)
Copied to clipboard
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
| Challenge: | Large-scale generative language models such as GPT-3 are competitive few-shot learners. |
| Approach: | They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities. |
| Outcome: | The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions. |
Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains. |
| Approach: | They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting . |
| Outcome: | The proposed model outperforms existing methods on a large set of annotated datasets. |
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue (2022.findings-acl)
Copied to clipboard
| Challenge: | Multi-SentAugment and LayerAgg are self-training methods that augment available training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora. |
| Approach: | They propose to use multi-sentaugment and layeragg to improve dialogue natural language understanding across multiple languages. |
| Outcome: | The proposed methods generalise well in zero- and few-shot scenarios and leverage external unannotated data sources. |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
Copied to clipboard
Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon (2024.eacl-long)
Copied to clipboard
| Challenge: | Prior work extended multilingual models to other languages due to the unavailability of labeled and unlabeled training data. |
| Approach: | They use multilingual lexicons to enhance multilingual models capabilities in low-resource languages . they focus on zero-shot sentiment analysis tasks across 34 languages based on a single sentence . |
| Outcome: | The proposed model improves zero-shot performance across 34 languages without using any sentence-level sentiment data. |
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing zero-shot dialogue state tracking datasets are limited in the number of domains and slot types they cover due to the high costs of data collection. |
| Approach: | They propose a fully automatic approach that generates synthetic zero-shot dialogue state tracking datasets. |
| Outcome: | The proposed approach can generate dialogues across 1,000+ domains with silver-standard dialogue state annotations and slot descriptions. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
Copied to clipboard
| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |